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Can anyone recommend a source for setting up an environment in Linux to run the code in this book? Do I need to learn eMacs to get close to a “modern lisp” programming environment?
Bear in mind that this book explicitly expects readers to already be familiar with lisp, so if you've not got a lisp environment going already and need an introduction, you should really look elsewhere first. Touretzsky's book is a great place to start. https://www.cs.cmu.edu/~dst/LispBook/
Earlier quoted context omitted.
When people talk about AI today they are usually talking about machine learning systems based in neural networks. The kind of symbolic AI described in this book went through several cycles of hype and disappointment to the point where many think it is obsolete. People often do it connect recent breakthroughs in SAT and SMT solvers with this history and for that matter production rules engines are dramatically better…
Hi @Paul, I’m a newbie in this field. Are you saying that this branch of AI isn’t relevant when compared to machine learning that’s based on neural networks?
Almost any financial institution has a copy of IBM iLOG in there somewhere implementing policy in terms of production rules.
Some of the most interesting systems today combine ideas from machine learning with ideas from AI search. For instance there are many game playing programs like AlphaGo that use
https://en.wikipedia.org/wiki/Monte_Carlo_tree_search
which runs a large number of games to the end rather than searching the next few moves exhaustively. Using a machine learning model to play the game for the playouts but sampling a large number of moves with A.I. search turns out to be a winning strategy.
Earlier quoted context omitted.
When people talk about AI today they are usually talking about machine learning systems based in neural networks. The kind of symbolic AI described in this book went through several cycles of hype and disappointment to the point where many think it is obsolete. People often do it connect recent breakthroughs in SAT and SMT solvers with this history and for that matter production rules engines are dramatically better…
Maybe if you define AI to be “technology that could plausibly lead to AGI”, but everything in this book is still terrifically relevant for many practical “how to get my computer to solve this semi-open ended search problem efficiently”. Which is far from uncommon.
My AI prof joked, I think, that it was "things that don't work yet" - clearly only a humanlike AI could do OCR... until it started working, etc.
But I actually think your hypothetically proposed definition fits the history even better.